File

Crimes_2001_to_present <- read_csv("Crimes_-_2001_to_present.csv", 
     col_types = cols(Latitude = col_character(), 
        Location = col_character(), Longitude = col_character(), 
        `X Coordinate` = col_character(), 
        `Y Coordinate` = col_character()))

Crimes_2001_to_present

Crimes <- Crimes_2001_to_present
Crimes08 <- Crimes %>% filter(Year=="2008")
Crimes08

Crimes08Type <- Crimes08 %>% group_by(`Primary Type`, `Location Description`) %>% summarise(count = n()) %>% rename(PriType=`Primary Type`, LocDes = `Location Description`)
Crimes08Type

Graph08 <- ggplot(data=Crimes08Type,aes(x=reorder(PriType,count),y=count ,fill=LocDes))+geom_bar(stat='identity',position='stack', width=.9)+theme(axis.text.x = element_text(angle = 45, hjust = 1))+scale_color_grey()
ggplotly(Graph08, height=1000, width=3000)

EachYear <- Crimes %>% filter(Year!=2020) %>% group_by(Year,`Primary Type`) %>% summarise(count=n()) %>% arrange(`Primary Type`) %>% rename(PriType=`Primary Type`)
EachYear

EachYearGraph <- ggplot(data=EachYear,aes(x=Year,y=count,fill=PriType))+geom_bar(stat='identity',position='stack', width=.9)+theme(axis.text.x = element_text(angle = 45, hjust = 1))+scale_color_grey()
ggplotly(EachYearGraph, height=1000, width=1500)

options(noaakey = "dZHCRTVsEdwFQEzgovApkrhRWtwHWjjJ")
ncdc(datasetid = 'GHCNDMS', stationid = 'GHCND:USW00014819', startdate = '2010-01-01',
   enddate = '2019-12-31')

$meta
$meta$totalCount
[1] 1200

$meta$pageCount
[1] 25

$meta$offset
[1] 1


$data

attr(,"class")
[1] "ncdc_data"

dat <- ghcnd(stationid = "USW00014819")

using cached file: /Users/kairui/Library/Caches/R/noaa_ghcnd/USW00014819.dly
date created (size, mb): 2020-04-24 21:35:18 (1.589)

dattemp <- dat %>%
  filter(element == "TMAX", year >= 2000)%>% select(VALUE1,year,month)%>%group_by(VALUE1=VALUE1/10)%>%group_by(AvgTemp=VALUE1)%>%select(year, month, AvgTemp)
dattemp

Crimes08 <- Crimes08 %>% mutate(Date = lubridate:: dmy_hms(Date))%>%arrange(Date)

 257218 failed to parse.

Crimes08

Crimes08 <- Crimes08 %>% group_by(`Primary Type`,month=floor_date(Date, "month")) %>% summarise(count = n()) %>% rename(PriType=`Primary Type`)
Crimes08

Crimes08Theft <- Crimes08 %>% filter(PriType=="THEFT")
Crimes08Theft

ggplot( data = dattemp, aes(x = AvgTemp)) + geom_density(adjust = 0.4) + labs(title = "")